Generative Adversarial Intelligence for Secure Digital Transformation: An Intelligent Threat Detection Framework for Data Driven Systems

The rapid digital transformation of data driven organizations has significantly increased the complexity and volume of cyber threats targeting modern database systems. Traditional rule based and signature driven security mechanisms often fail to detect sophisticated and evolving attack patterns in dynamic digital environments. To address this challenge, this paper proposes an intelligent threat detection framework based on Generative Adversarial Networks (GANs) to enhance security in digitally transformed infrastructures. The proposed approach leverages the generative capability of GANs to model normal and malicious data behaviors, enabling the system to identify anomalous and previously unseen threats with high accuracy. By integrating deep learning components for feature extraction and adversarial training, the framework improves detection performance while reducing false positives in large scale database environments. Experimental evaluations conducted on representative datasets demonstrate that the proposed model outperforms conventional machine learning and deep learning based security solutions in terms of detection accuracy, robustness, and adaptability. The results highlight the potential of generative artificial intelligence as a key enabler for secure digital transformation, supporting intelligent decision making and resilience in modern information systems. This study contributes to the growing body of research on applying generative AI techniques to cybersecurity challenges within digital transformation ecosystems.

کلیدواژه ها:

Generative Artificial Intelligence ، Digital Transformation ، Threat Detection ، Generative Adversarial Networks ، Database Security ، Intelligent Systems ، Cybersecurity